Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA259241 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:LASSIM -a network inference toolbox for genome-wide mechanistic modeling
| PRJNA259237 | ENA
Project description:LASSIM -a network inference toolbox for genome-wide mechanistic modeling [MAF, MYB & GATA3 siRNA]
| PRJNA275286 | ENA
Project description:LASSIM -a network inference toolbox for genome-wide mechanistic modeling [USF2, KLF6/COPEB, CBL and HIS1 siRNA]]
| PRJNA377298 | ENA
Project description:We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.
2017-06-12 | GSE65880 | GEO
Project description:We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.
2017-06-12 | GSE95508 | GEO
Project description:We present LASSIM, which is a toolbox built to build and infer parameters within mechanistic models on a genomic scale. This is made possible due to a property shared across biological systems, namely the existence of a subset of master regulators, here denoted the core system. The introduction of a core system of genes simplifies the inference into small solvable sub-problems, and implies that all main regulatory actions on peripheral genes come from a small set of regulator genes. This separation allows substantial parts of computations to be solved in parallel, i.e. permitting the use of a computer cluster, which substantially reduces the time for the computation to finish.
2017-06-12 | GSE60683 | GEO
Project description:Recent and ongoing revolutions in measurement technologies imply completely new possibilities for genome research: today, time-resolved, quantitative, and systems-level data are available. Nevertheless, without a corresponding revolution in methods for data analysis, these new data tend to drown researchers and doctors, rather than provide clear and useful insights. Such new methods are developed within the field of systems biology. Systems biology has two main approaches: mechanistically detailed and well-determined simulation models for small subsystems, and more approximative statistical models for the entire genome. However, there are few, if any, methods that combine the strengths of these two approaches. Herein, we present LASSIM, a new simulation-based approach, which can be applied to systems of the size of the entire genome. The superior performance of LASSIM is demonstrated in three examples: i) an example with simulated data shows that unlike traditional large-scale methods, LASSIM correctly identifies the true behavior between measured data-points, ii) LASSIM outperforms the winner of a previous DREAM challenge, the most competitive benchmarking approach available, iii) based on new data from TH2 differentiation, LASSIM identifies a first mechanistic model for the entire genome. The key predictions of this model are typically enriched for DNA bindings, which suggests that most predicted interactions are direct. Moreover, in silico knockdowns were experimentally validated. In summary, LASSIM opens the door to a new type of model-based data analysis: to models that combine the strengths of reliable mechanistic models with truly systems-level data.
2017-06-12 | GSE60681 | GEO
Project description:Recent and ongoing revolutions in measurement technologies imply completely new possibilities for genome research: today, time-resolved, quantitative, and systems-level data are available. Nevertheless, without a corresponding revolution in methods for data analysis, these new data tend to drown researchers and doctors, rather than provide clear and useful insights. Such new methods are developed within the field of systems biology. Systems biology has two main approaches: mechanistically detailed and well-determined simulation models for small subsystems, and more approximative statistical models for the entire genome. However, there are few, if any, methods that combine the strengths of these two approaches. Herein, we present LASSIM, a new simulation-based approach, which can be applied to systems of the size of the entire genome. The superior performance of LASSIM is demonstrated in three examples: i) an example with simulated data shows that unlike traditional large-scale methods, LASSIM correctly identifies the true behavior between measured data-points, ii) LASSIM outperforms the winner of a previous DREAM challenge, the most competitive benchmarking approach available, iii) based on new data from TH2 differentiation, LASSIM identifies a first mechanistic model for the entire genome. The key predictions of this model are typically enriched for DNA bindings, which suggests that most predicted interactions are direct. Moreover, in silico knockdowns were experimentally validated. In summary, LASSIM opens the door to a new type of model-based data analysis: to models that combine the strengths of reliable mechanistic models with truly systems-level data.
2024-01-26 | GSE56434 | GEO
Project description:A generic genome-scale metabolic model (GEMs) of human CD4+ T-cells. Several cell-specific GEMs for CD4+ T-cell subsets such as Th1, Th2, Th17 and iTreg cells derived from "HTimmR" are included as additional files. The model formats are compatible with RAVEN v.2.0 toolbox.
2023-03-29 | MODEL2101270002 | BioModels
Project description:Systemic sclerosis (SSc) poses a significant challenge in autoimmunology, characterized by the development of debilitating fibrosis of skin and internal organs. The pivotal role of dysregulated T cells, notably the skewed polarization toward Th2 cells, has been implicated in the vascular damage and progressive fibrosis observed in SSc. In this study, we explored the underlying mechanisms by which cannabinoid receptor 2 (CB2) highly selective agonist HU-308 restores the imbalance of T cells to alleviate SSc. Using a bleomycin-induced SSc (BLM-SSc) mouse model, we demonstrated that HU-308 effectively attenuates skin and lung fibrosis by specifically activating CB2 on CD4+ T cells to inhibit the polarization of Th2 cells in BLM-SSc mice, which was validated by Cnr2-specific-deficient mice. Different from classical signaling downstream of G protein-coupled receptors (GPCRs), HU-308 facilitates the expression of SOCS3 protein and subsequently impedes the IL2/STAT5 signaling pathway during Th2 differentiation. The deficiency of SOCS3 partially mitigated the impact of HU-308. Analysis of a cohort comprising 62 SSc patients and 82 healthy controls revealed an abnormal elevation in the Th2/Th1 ratio in SSc patients. Administration of HU-308 to PBMCs and peripheral CD4+ T cells from SSc patients led to the upregulation of SOCS3, which effectively suppressed the aberrantly activated STAT5 signaling pathway and the proportion of CD4+IL4+ T cells. In conclusion, our findings unveil a novel mechanism by which the CB2 agonist HU-308 ameliorates fibrosis in SSc by targeting and reducing Th2 responses. These insights provide a foundation for future therapeutic approaches in SSc by modulating Th2 responses.
2025-02-27 | GSE261552 | GEO